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Graph kernel of brain networks considering functional similarity measures
Xinlei Wang1, Junchang Xin2, Zhongyang Wang3
1School of Computer Science and Engineering, Northeastern University, 110169, China.
Computers in Biology and Medicine
|February 17, 2024
Summary
This study introduces novel graph kernel methods that integrate both structural and functional brain network information for improved neurodegenerative disease diagnosis. These advanced techniques enhance diagnostic accuracy by considering multi-attribute and higher-order correlations in brain networks.
Area of Science:
- Neuroscience
- Computational Biology
- Medical Informatics
Background:
- Graph kernels are vital for brain network analysis and neurodegenerative disease diagnosis by measuring brain network similarity.
- Existing methods primarily use structural similarity, neglecting crucial functional information like brain region attributes and connectivity.
- This oversight limits the precise localization of disease-affected areas within brain networks.
Purpose of the Study:
- To propose a multi-attribute graph kernel that incorporates functional information into brain network analysis.
- To develop a multi-attribute hypergraph kernel capturing higher-order correlations between brain regions.
- To enhance the accuracy of neurodegenerative disease diagnosis by leveraging both structural and functional similarities.
Main Methods:
- Developed a multi-attribute graph kernel assigning multiple attributes to brain network nodes, utilizing the Weisfeiler-Lehman color refinement algorithm.
- Proposed a multi-attribute hypergraph kernel to account for functional and structural similarities and higher-order correlations.
- Validated the methods on real-world datasets for neurodegenerative disease diagnosis.
Main Results:
- The proposed multi-attribute graph and hypergraph kernels significantly improved the performance of neurodegenerative disease diagnosis.
- Experimental results demonstrated superior diagnostic accuracy compared to existing methods.
- Statistical tests confirmed the significant difference and effectiveness of the novel approaches.
Conclusions:
- Integrating multi-attribute functional and structural information enhances brain network analysis for disease diagnosis.
- The proposed graph and hypergraph kernels offer a more comprehensive approach to understanding brain network alterations in neurodegenerative diseases.
- These methods represent a significant advancement in computational neuroscience for clinical applications.
